Methods for selecting microbes from a diverse genetically modified library to detect and optimize the production of metabolites

ABSTRACT

The present invention relates to genetically modified bacteria and methods of optimizing genetically modified bacteria for the production of a metabolite.

RELATED APPLICATION DATA

This application is a continuation application which claims priority toU.S. patent application Ser. No. 14/775,025, filed on Sep. 11, 2015,which is a National Stage Application under 35 U.S.C. 371 of co-pendingPCT Application No. PCT/US14/18616 designating the United States andfiled Feb. 26, 2014; which claims the benefit of U.S. ProvisionalApplication No. 61/781,373 and filed Mar. 14, 2013 each of which arehereby incorporated by reference in their entireties.

STATEMENT OF GOVERNMENT INTERESTS

This invention was made with government support under DE-FG02-02ER63445awarded by the U.S. Department of Energy. The government has certainrights in the invention.

FIELD

The present invention relates in general to genetically modifiedbacteria and methods of optimizing genetically modified bacteria for theproduction of a metabolite.

BACKGROUND

Advances in genome engineering techniques of microbes have enabledfacile, multiplexed modification of biosynthetic pathway genes tomaximize production of high-value chemicals in the host organism.Selecting the successful strain among a large population of genotypesremains a major challenge. Accordingly, it is desirable to developmethods of identifying strains that are optimized for expression ofexogenous DNA.

SUMMARY

Embodiments of the present disclosure are directed to methods ofidentifying a bacterial strain that is optimized for production of ametabolite from a population of bacterial strains. The methods describedherein can be used to quickly identify the best strains for chemicalproduction out of millions of less effective strains. Embodimentsdescribed herein are intended to be applicable to a broad range ofchemicals that can be synthesized by microorganisms having their genomesgenetically modified to include the synthetic pathway for a desiredchemical.

According to one aspect, the genome of a microorganism is geneticallymodified to produce a recombinant microorganism by insertion into themicroorganism's genome a DNA sequence, such as a synthetic DNA sequence,encoding a metabolite binding molecule (referred to herein as a“sensor”). According to one aspect, the sensor or metabolite bindingmolecule is an allosteric biomolecule that undergoes a conformationchange upon binding a desired chemical or metabolite resulting in achange in gene regulation. Sensors and their corresponding bindingpartners are known to those of skill in the art and include allostericmolecules such as transcription factors (which bind to DNA to regulateexpression of the bound DNA sequence), riboswitches, two-componentsignaling proteins and nuclear hormone receptors.

The genome of the microorganism is also genetically modified to includeDNA encoding for an antidote to a toxin. When expressed, the sensorregulates the production of the antidote within the microorganism.Depending on the nature of the sensor, it can regulate antidoteproduction by repressing in the absence of the metabolite, activating inthe presence of the metabolite, occluding ribosome binding site in theabsence of metabolite etc. If the microorganism is placed into anenvironment of the toxin and no antidote or insufficient antidote isproduced, the microorganism will die.

The microorganism has also been genetically modified to include DNAencoding genes to produce a metabolite binding partner of the sensor.Alternatively, endogenous genes in the microbe can produce themetabolite. The metabolite binding partner is a target chemical desiredto be produced by the microorganism. The sensor which can be a DNAbinding molecule will bind to the metabolite, when expressed. In thismanner, the genetically modified microorganism can sense its own levelof chemical production insofar as the sensor can sense for the presencewithin the microorganism of the metabolite. When the metabolite isproduced by the cell, the metabolite binds to the sensor in a manner toregulate the antidote gene and, as a result, antidote is produced by themicroorganism proportional to the amount of metabolite binding partnerproduced by the microorganism.

The microorganism is placed into an environment of a toxin counterpartto the antidote. In this manner, the antidote is referred to herein as a“selector” to the extent that antidote is produced by the cell inresponse to the level of metabolite present and in an amount sufficientto prevent the cell from dying. The level of antidote, which isproportional to the level of metabolite, selects strains for furthermodification and optimization. Microorganisms within a population ofmicroorganisms that make more metabolite binding partner produce moreantidote thereby promoting cell survivability. The concentration oftoxin can be increased for a given strain to determine what level oftoxin will result in cell death. In this manner, a strain can beselected for a given production of antidote, and accordingly, a givenproduction of metabolite.

According to one aspect, a selected strain is subjected to geneticmodification intended to optimize metabolite production by diversifyingthe population of microorganisms with a large number of semi-randomchemical production designs, typically on the order of a billion. Agenetically modified strain can be selected for its ability to produceantidote and therefore metabolite. A selected strain can be subjected torepeated rounds of genetic modification and selection in a toxinenvironment to produce a strain with optimized metabolite production. Astoxin concentration is increased, only those genetically modifiedstrains that produce sufficient metabolite, and therefore, antidote areable to survive. With each round of genetic modification and increasedtoxin concentration, a more robust metabolite producing strain isselected until the strain is optimized for metabolite production.Accordingly, an additional aspect includes identifying a strain that isoptimized for production of the metabolite by identifying survivingstrains subjected to increasing concentrations of toxin. A series ofgenetic selections query the level of antidote protein eachmicroorganism is producing. Microorganisms are killed that have anantidote protein level insufficient to detoxify the microorganism.Microorganisms survive that have an antidote protein level sufficient todetoxify the microorganism.

According to one aspect, a method of selecting a subset ofmicroorganisms for the production of a metabolite is provided whichincludes placing a population of microorganisms in an environment of atoxin, wherein the population of microorganisms has been geneticallymodified to include exogenous DNA encoding for an antidote to the toxin,wherein the population of microorganisms has been genetically modifiedto include exogenous DNA encoding a sensor which when expressed inhibitsproduction of the antidote by the microorganisms, wherein the populationof microorganisms has been genetically modified (may or may not begenetically modified) to include exogenous DNA encoding pathway genes tometabolite binding partner of the sensor or which may already includeDNA encoding pathway genes to a metabolite binding partner, which whenexpressed binds to the DNA binding molecule to induce production of theantidote in a manner dependent on the concentration of the expressedmetabolite, and selecting a subset of microorganisms that producesufficient metabolite to prevent microbe death.

According to one aspect, the method further includes geneticallymodifying the subset of microorganisms to alter genes that produce themetabolite or to alter related metabolism, subjecting the subset ofmicroorganisms to a subsequent environment of the toxin having aconcentration greater than the environment, and selecting a subsequentsubset of microorganisms the produce sufficient metabolite to preventmicroorganism death.

According to one aspect, the method further comprises repeating insequence (1) genetically modifying the subsequent subset ofmicroorganisms by altering genes that produce the metabolite or byaltering related metabolism, (2) subjecting the genetically alteredmicroorganisms to a subsequent environment of a toxin having aconcentration greater than a previous environment, and (3) selecting afurther subsequent subset of microorganisms that produce sufficientmetabolite to prevent microorganism death, said repeating step resultingin optimized metabolite producing microorganism.

According to one aspect, the sensor is a transcription factor,riboswitch, two-component signaling protein or a nuclear hormonereceptor.

According to one aspect, the binding of the metabolite to the sensoractivates gene expression to induce production of the antidote in amanner dependent on the concentration of the expressed metabolite.

According to one aspect, a positive selection marker is used as theantidote to select the subset of microorganisms that produce sufficientmetabolite to prevent microorganism death.

According to one aspect, a dual selection marker is used to eliminatefalse positives and to select the subset of microorganisms that producesufficient metabolite to prevent microorganism death.

According to one aspect, binding of the metabolite to the DNA bindingprotein represses gene expression to induce production of the antidotein a manner dependent on the concentration of the expressed metabolite.

According to one aspect, a negative selection marker is used toeliminate the subset of microorganisms that are false positives (i.e.,detoxify despite not producing sufficient metabolite).

According to one aspect, the population of microorganisms have beengenetically modified to include an additional exogenous DNA encoding thesensor which when expressed inhibits production of the antidote by themicroorganisms.

According to one aspect, the microorganisms express a degradation tag toincrease the degradation rate for antidote within the microorganisms toreduce false positives.

According to one aspect, translation of the sensor is attenuated toreduce false positives.

According to one aspect, the step of genetically modifying the subset ofmicroorganisms to alter genes that produce the metabolite includesmultiplexed automated genome engineering.

According to one aspect, the multiplexed automated genome engineeringincludes reducing spontaneous background mutants.

According to one aspect, the multiplexed automated genome engineeringincludes reducing spontaneous background mutants by pretreatment with anegative selector.

According to one aspect, the microorganisms have an escape rate of about1 in 10 million.

According to one aspect, translation of the antidote is attenuated toreduce false positives.

According to one aspect, two or more distinct copies of the sensorbiomolecule are expressed to reduce the rate of escape caused by geneticmutations that may inactivate a single copy of the sensor.

According to one aspect, the sensor biomolecule may regulate its ownexpression in addition to regulating the expression of the antidote.

According to one aspect, the sensor may regulate the expression of twoor more distinct antidote proteins that confer survival in the presenceof two or more distinct toxins.

According to one aspect, a method is providing for selecting a subset ofmicroorganisms for the production of a metabolite which includes placinga population of microorganisms in an environment of a toxin, wherein thepopulation of microorganisms have been genetically modified to includeexogenous DNA encoding for an antidote to the toxin, wherein thepopulation of microorganisms have been genetically modified to includeexogenous DNA encoding a sensor which when expressed regulatesproduction of the antidote by the microorganisms, wherein the populationof microorganisms may or may not have been genetically modified toinclude pathway genes to produce a metabolite binding partner of thesensor, which when expressed binds to the sensor to induce production ofthe antidote in a manner dependent on the concentration of the expressedmetabolite, repeatedly genetically modifying the microorganisms to altergenes that produce the metabolite, subjecting the microorganisms tonegative selection, transforming surviving microorganisms with a plasmidincluding remaining exogenous DNA to complete the pathway to produce themetabolite, selecting microorganisms including the plasmid, andselecting a subset of microorganisms that produce sufficient metaboliteto prevent microorganism death.

According to one aspect, a method is for reducing the false positives bynegative selection after diversity generation by multiplex automatedgenome engineering or other methods, then subsequently transformingpathway complete gene(s) before applying positive selection.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages of the present inventionwill be more fully understood from the following detailed description ofillustrative embodiments taken in conjunction with the accompanyingdrawing in which:

FIG. 1 is a schematic depicting selector options where the choice ofselector depends of mode of gene regulation of sensor.

FIG. 2A depicts in schematic an inverter circuit illustrated with B12riboswitch. FIG. 2B is a graph of a B12 response curve with invertercircuit.

FIG. 3 is a dual gradient heat plot describing cell survivability as afunction of metabolite concentration. Dark color is growth and lightcolor is no growth. Naringenin sensed by TtgR against a toxin SDSconcentration gradient.

FIG. 4 is graph of probability of failure of 10 sensor-selector modulesas a measure of the escape rate of sensor-selector strains.

FIG. 5 is a schematic illustrating methods to reduce the failure ratesof sensor-selectors.

FIG. 6 is a graph comparing failure rates of TtgR_tolC and dual copy ofTtgR with tolC. The failure rate drops by nearly four orders ofmagnitude.

FIG. 7 is a graph illustrating that the addition of degradation tag toselector reduces false positive rate. The figure compares theprobability of failure of TtgR_tolC with four degradation tag variantsof different strengths.

FIG. 8 is a graph illustrating the effect of prescreening with negativeselection to eliminate false positives. The graph shows a comparison offailure of rate of glucaric acid pathway parent strain and the samestrain after 5 cycles of MAGE and treated with different concentrationof colicin.

FIG. 9 is a graph of the dynamic range of TtgR. The arrow denotes thedynamic range of TtgR for metabolite genestein.

FIG. 10 is a graph of the dynamic range of ten sensor-selectors andtheir cognate metabolites.

FIG. 11 is a schematic of the ToSLIMM protocol. The five panels describethe steps in the ToSLIMM protocol. (A) Starting population, last fewpathway gene(s) removed. (B) Diversified population after MAGE. The redspots represent genomic changes. The orange cells are spontaneousmutants that result in false positives. (C) Negative selectioneliminates false positives. (D) The pathway genes containing plasmid isintroduced into the cells surviving negative selection. The pathway isnow complete and metabolite is produced. (E) After positive selection,the best producer is isolated.

FIG. 12 is a graph of naringenin production levels for parent strain(CTRL1, CTRL2) and clones selected after MAGE (C6-D12). Production wasmeasured using liquid chromatography-mass spectrometry and is reportedas raw ion counts (arbitrary units). Highest production clone produces12-fold more than parent strain.

FIG. 13 is a graph of glucaric acid production levels for parent strain(SSECcdaR) and clones after MAGE mutagenesis and selection(SSECcdaR.1-10). Production level for the highest clone is 415-foldhigher than the parent strain.

DETAILED DESCRIPTION

Embodiments of the present disclosure include a recombinant hostmicroorganism that includes one or more genetic modifications whichprogram the microorganism to produce an exogenous sensor, a metabolitebinding partner and an exogenous antidote to a toxin. When expressed thesensor regulates production of the antidote. When the pathway genes areexpressed, the metabolite binding partner is produced which then bindsto the sensor and promotes production of the antidote in proportion tothe amount of metabolite.

Using this recombinant microorganism, a method is provided for selectinga recombinant strain that produces high amounts of the metabolite. Therecombinant microorganism is placed into an environment of the toxin. Ifthe recombinant microorganism produces sufficient antidote, which isproportional to the amount of metabolite produced, the strain survivesand is selected as a suitable strain for the production of themetabolite. This selected strain can be subjected to repeated rounds ofgenetic modification (such as by using multiplexed automated genomeengineering) designed to improve metabolite production and selection inresponse to a toxin level to create a recombinant strain optimized formetabolite production.

Standard recombinant DNA and molecular cloning techniques used hereinare well known in the art and are described in Sambrook, J., Fritsch, E.F. and Maniatis, T., Molecular Cloning: A Laboratory Manual, 2^(nd) ed.;Cold Spring Harbor Laboratory: Cold Spring Harbor, N.Y., (1989) and bySilhavy, T. J., Bennan, M. L. and Enquist, L. W., Experiments with GeneFusions; Cold Spring Harbor Laboratory: Cold Spring Harbor, N.Y.,(1984); and by Ausubel, F. M. et. al., Current Protocols in MolecularBiology, Greene Publishing and Wiley-Interscience (1987) each of whichare hereby incorporated by reference in their entireties.

Additional useful methods are described in manuals including AdvancedBacterial Genetics (Davis, Roth and Botstein, Cold Spring HarborLaboratory, 1980), Experiments with Gene Fusions (Silhavy, Berman andEnquist, Cold Spring Harbor Laboratory, 1984), Experiments in MolecularGenetics (Miller, Cold Spring Harbor Laboratory, 1972) ExperimentalTechniques in Bacterial Genetics (Maloy, in Jones and Bartlett, 1990),and A Short Course in Bacterial Genetics (Miller, Cold Spring HarborLaboratory 1992) each of which are hereby incorporated by reference intheir entireties.

Microorganisms may be genetically modified to delete genes orincorporate genes by methods known to those of skill in the art. Vectorsand plasmids useful for transformation of a variety of host cells arecommon and commercially available from companies such as InvitrogenCorp. (Carlsbad, Calif.), Stratagene (La Jolla, Calif.), New EnglandBiolabs, Inc. (Beverly, Mass.) and Addgene (Cambridge, Mass.).

Typically, the vector or plasmid contains sequences directingtranscription and translation of a relevant gene or genes, a selectablemarker, and sequences allowing autonomous replication or chromosomalintegration. Suitable vectors comprise a region 5′ of the gene whichharbors transcriptional initiation controls and a region 3′ of the DNAfragment which controls transcription termination. Both control regionsmay be derived from genes homologous to the transformed host cell,although it is to be understood that such control regions may also bederived from genes that are not native to the species chosen as aproduction host.

Initiation control regions or promoters, which are useful to driveexpression of the relevant pathway coding regions in the desired hostcell are numerous and familiar to those skilled in the art. Virtuallyany promoter capable of driving these genetic elements is suitable forthe present invention including, but not limited to, lac, ara, tet, trp,IP_(L), IP_(R), T7, tac, and trc (useful for expression in Escherichiacoli and Pseudomonas); the amy, apr, npr promoters and various phagepromoters useful for expression in Bacillus subtilis, and Bacilluslicheniformis; nisA (useful for expression in Gram-positive bacteria,Eichenbaum et al. Appl. Environ. Microbiol. 64(8):2763-2769 (1998)); andthe synthetic P11 promoter (useful for expression in Lactobacillusplantarum, Rud et al., Microbiology 152:1011-1019 (2006)). Terminationcontrol regions may also be derived from various genes native to thepreferred hosts.

Certain vectors are capable of replicating in a broad range of hostbacteria and can be transferred by conjugation. The complete andannotated sequence of pRK404 and three related vectors-pRK437, pRK442,and pRK442(H) are available. These derivatives have proven to bevaluable tools for genetic manipulation in Gram-negative bacteria (Scottet al., Plasmid 50(1):74-79 (2003)). Several plasmid derivatives ofbroad-host-range Inc P4 plasmid RSF1010 are also available withpromoters that can function in a range of Gram-negative bacteria.Plasmid pAYC36 and pAYC37, have active promoters along with multiplecloning sites to allow for the heterologous gene expression inGram-negative bacteria.

Chromosomal gene replacement tools are also widely available. Forexample, a thermosensitive variant of the broad-host-range repliconpWV101 has been modified to construct a plasmid pVE6002 which can beused to create gene replacement in a range of Gram-positive bacteria(Maguin et al., J. Bacteriol. 174(17):5633-5638 (1992)). Additionally,in vitro transposomes are available to create random mutations in avariety of genomes from commercial sources such as EPICENTRE® (Madison,Wis.).

Vectors useful for the transformation of E. coli are common andcommercially available. For example, the desired genes may be isolatedfrom various sources, cloned onto a modified pUC19 vector andtransformed into E. coli host cells. Alternatively, the genes encoding adesired biosynthetic pathway may be divided into multiple operons,cloned onto expression vectors, and transformed into various E. colistrains.

The Lactobacillus genus belongs to the Lactobacillales family and manyplasmids and vectors used in the transformation of Bacillus subtilis andStreptococcus may be used for Lactobacillus. Non-limiting examples ofsuitable vectors include pAM.beta.1 and derivatives thereof (Renault etal., Gene 183:175-182 (1996); and O'Sullivan et al., Gene 137:227-231(1993)); pMBB1 and pHW800, a derivative of pMBB1 (Wyckoff et al. Appl.Environ. Microbiol. 62:1481-1486 (1996)); pMG1, a conjugative plasmid(Tanimoto et al., J. Bacteriol. 184:5800-5804 (2002)); pNZ9520(Kleerebezem et al., Appl. Environ. Microbiol. 63:4581-4584 (1997));pAM401 (Fujimoto et al., Appl. Environ. Microbiol. 67:1262-1267 (2001));and pAT392 (Arthur et al., Antimicrob. Agents Chemother. 38:1899-1903(1994)). Several plasmids from Lactobacillus plantarum have also beenreported (van Kranenburg R, Golic N, Bongers R, Leer R J, de Vos W M,Siezen R J, Kleerebezem M. Appl. Environ. Microbiol. 2005 March; 71(3):1223-1230), which may be used for transformation.

Initiation control regions or promoters, which are useful to driveexpression of the relevant pathway coding regions in the desiredLactobacillus host cell, may be obtained from Lactobacillus or otherlactic acid bacteria, or other Gram-positive organisms. A non-limitingexample is the nisA promoter from Lactococcus. Termination controlregions may also be derived from various genes native to the preferredhosts or related bacteria.

The various genes for a desired biosynthetic or other desired pathwaymay be assembled into any suitable vector, such as those describedabove. The codons can be optimized for expression based on the codonindex deduced from the genome sequences of the host strain, such as forLactobacillus plantarum or Lactobacillus arizonensis. The plasmids maybe introduced into the host cell using methods known in the art, such aselectroporation, as described in any one of the following references:Cruz-Rodz et al. (Molecular Genetics and Genomics 224:1252-154 (1990)),Bringel and Hubert (Appl. Microbiol. Biotechnol. 33: 664-670 (1990)),and Teresa Alegre, Rodriguez and Mesas (FEMS Microbiology letters241:73-77 (2004)). Plasmids can also be introduced to Lactobacillusplantatrum by conjugation (Shrago, Chassy and Dobrogosz Appl. Environ.Micro. 52: 574-576 (1986)). The desired biosynthetic pathway genes canalso be integrated into the chromosome of Lactobacillus usingintegration vectors (Hols et al. Appl. Environ. Micro. 60:1401-1403(1990); Jang et al. Micro. Lett. 24:191-195 (2003)).

Microorganisms which may serve as host cells and which may begenetically modified to produce recombinant microorganisms as describedherein may include one or members of the genera Clostridium,Escherichia, Rhodococcus, Pseudomonas, Bacillus, LactobacillusSaccharomyces, and Enterococcus. Particularly suitable microorganismsinclude Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae.

According to certain aspects, a microorganism is genetically modified toinclude one or more exogenous nucleic acids encoding for a sensor andits corresponding metabolite binding partner. Sensors are known to thoseof skill in the art and include transcription factors, riboswitches,two-component signaling proteins and nuclear hormone receptors.Exemplary sensor biomolecules, class type and their correspondingmetabolite binding partners are listed in Table 1 below.

TABLE 1 Sensor Gene Molecule Type of Sensor cdaR glucaric acidTranscriptional activator ttgR naringennin Transcriptional repressor(flavanoids) btuB riboswitch cobalamin Riboswitch mphR macrolidesTranscriptional repressor tetR tetracycline Transcriptional repressorderivates benM muconic acid Transcriptional activator alkS medium chainn- Transcriptional activator alkanes xylR xylose Transcriptionalactivator araC Arabinose Transcriptional activator gntR GluconateTranscriptional repressor galS Galactose Transcriptional repressor trpRtryptophan Transcriptional repressor qacR Berberine Transcriptionalrepressor rmrR Phytoalexin Transcriptional repressor cymR CumateTranscriptional repressor melR Melibiose Transcriptional activator rafRRaffinose Transcriptional activator nahR Salicylate Transcriptionalactivator nocR Nopaline Transcriptional activator clcR ChlorobenzoateTranscriptional activator varR Virginiamycin Transcriptional repressorrhaR Rhamnose Transcriptional repressor PhoR Phosphate Two-componentsystem MalK Malate Two-component system GlnK Glutamine Two-componentsystem Retinoic acid Retinoic acid Nuclear hormone receptor receptorEstrogen receptor Estrogen Nuclear hormone receptor Ecdysone receptorEcdysone Nuclear hormone receptor

It is to be understood that the examples of sensors and theircorresponding metabolite binding partners are exemplary only and thatone of skill in the art can readily identify additional sensors andtheir corresponding metabolite binding partners for use in the presentdisclosure. The transformed microorganism is intended to express thesensors and the metabolite under suitable conditions.

The biosynthetic pathways for production of any particular metabolitebinding partner are known to those of skill in the art. The sensorsequence is identified based on published literature search. Forexample, biosynthetic pathways for the above metabolite binding partnersand sensors are fully described in the following: cdaR (Monterrubio etal. 2000 J. Bacteriol 182(9):2672-4), tetR (Lutz and Bujard NucleicAcids Res. 1997 25(6):1203-10), alkS (Canosa et al. Mol Micriobiol 200035(4):791-9), ttgR (Teran, et al. Antimicrob Agents Chemother.47(10):3067-72 (2003)), btuB riboswitch (Nahvi, et al. Nucleic AcidsRes. 32:143-150 (2004)); glucaric acid (Moon, et al. Appl Env Microbiol.75:589-595 (2009)), naringenin (Santos, et al. Metabolic Engineering.13:392-400 (2011)), alkanes (Steen, et al. 463:559-562 (2009)),cobalamin (Raux, et al. Cell Mol Life Sci. 57:1880-1893. (2000)),muconic acid (Niu, et al. Biotechnol Prog. 18:201-211. (2002)). Methodsdescribed herein can be used to insert the nucleic acids into the genomeof the microorganism that are responsible for production of sensors andmetabolite binding partners.

According to certain aspects, a microorganism is genetically modified toinclude one or more exogenous nucleic acids encoding an antidote to atoxin. Antidote and toxin pairs are known to those of skill in the artand include SDS:tolC, colicin:tolC (negative selection),kanamycin:kanamycin nucleotidyltransferase,chloramphenicol:chloramphenicol acyl tranferase, ampicillin:betalactamase, tetracycline:tetracycline efflux pump tetA, nickelchloride:tetracycline efflux pump tetA (negative selection),5-fluoroorotic acid: URA3 (negative selection). The transformedmicroorganism is intended to express the antidote under suitableconditions.

The genes for production of any particular antidote are known to thoseof skill in the art. For example, the genes for the above antidotes arefully described in tetA (Postle et al. Nucleic Acid Research 198412(12)4849-4863) tolC (Fralick J. Bacteriol 1996 178(19)5803-5805)Chloramphenicol acetyl tranfersase (Shaw et al. J Bacteriol. 1970104(3):1095-1105). Methods described herein can be used to insert thenucleic acids into the genome of the microorganism that are responsiblefor production of DNA binding molecules and metabolite binding partners.

According to one aspect, the transformed, recombinant microorganismexpresses the sensor which regulates production of the antidote. Whenexpressed, the sensor prevents the cell from expressing the antidotegene, either by blocking the expression (i.e. a repressor) or failing toactivate the expression (i.e. activator) of the antidote unless thesensor is bound by the target metabolite, which leads to antidoteexpression by changing sensor function. Several regulation mechanismsare possible: for an allosteric transcription factor that is arepressor, the repressor protein blocks transcription of the antidotegene by binding a region of DNA 5′ to the antidote gene unless thedesired metabolite binds the repressor; for an allosteric transcriptionfactor that is an activator, the activator recruits RNA polymerase to aregion of DNA 5′ to the antidote gene only when the desired metabolitebinds to the activator; for an attenuating riboswitch, the riboswitch isencoded in the 5′ untranslated region of a repressor regulating thetranscription of the antidote gene, and attenuates translation of thisrepressor when bound to the target metabolite (See FIG. 2 ). Accordingto a further aspect, the transformed, recombinant microorganismexpresses the biosynthetic genes that produce the metabolite which bindsto the sensor in a manner to promote production of the antidote.According to one aspect, the production of the antidote is proportionalto the amount of metabolite binding partner that is produced by themicroorganism and bound to sensor. In the absence of the metabolite, thesensor prevents production of antidote. Many individual sensor moleculesre expressed in the cell. The binding of the metabolite to a sensormolecule is a reversible event, and switches that individual sensormolecule from a state in which it prevents antidote expression to astate in which it allows antidote expression. When the concentration ofmetabolite is low, the proportion of the sensor molecules bound tometabolite is low at any given time, hence the antidote is not expressedor expressed only slightly. As the concentration of metaboliteincreases, the proportion of sensor molecules bound to metaboliteincreases, which results in higher expression of antidote. This givesrise to dose-dependence of the antidote production and metabolite level.That is, the more metabolite binding partner that is produced by thecell, the higher the proportion of sensor molecules bound to metabolitemolecules the cell to produce more antidote. The metaboliteconcentration below which there is no production of antidote is thedetection threshold, and the metabolite concentration above which thereis no further production of the antidote is the saturation point; theselimits give rise to the dynamic range of the sensor-antidote system.

According to one aspect, a microorganism genetically modified asdescribed herein to include exogenous nucleic acids which express a DNAbinding molecule, a metabolite binding partner and an antidote to atoxin can be used to identify an optimum strain for production of themetabolite, since production of the antidote is proportional toproduction of the antidote. The recombinant microorganism is placed intoa growth environment that includes a given concentration of toxin. Ifthe microorganism does not produce enough antidote to counter the toxin,the microorganism will die. If the microorganism produces enoughantidote to counter the toxin, the microorganism will live. Thesurviving microorganism is selected as a suitable producer of themetabolite. The selected microorganism strain can then be subjected torepeated rounds of multiplexed automated genome engineering targeted atgenetic modifications intended to increase metabolite production by themicroorganism. With each round, the genetically modified microorganismis subjected to increased levels of toxin and surviving strains areselected until a strain is identified having a desirable level ofmetabolite production. The strain can then be used to produce themetabolite, such as under large scale commercial settings.

An additional aspect of the present disclosure includes methods forlowering the escape rate of a population of recombinant microorganisms.That is, the number of genetically modified microorganisms that survivea toxin environment for reasons other than sufficient metaboliteproduction to induce antidote production is lowered or reduced. Thisprevents the selection of surviving strains that do not produceincreased levels of metabolite binding partners.

Example I Building a Sensor-Selector Constructing and Inserting into E.coli Genome

To build a sensor-selector, a single copy of the nucleic acid(s)encoding the DNA binding molecule (sensor) is inserted into the E. coligenome to minimize noise arising from copy number variability inplasmids. For a DNA binding molecule, its cognate promoter-operatorregulates the antidote (selector), or alternatively a riboswitch isplaced at the 5′ end of the selector transcript. The choice of theselector depends on the mode of gene regulation by the sensor. As shownin FIG. 1 , in response to the metabolite, if the sensor activates geneexpression (e.g., lad or theophylline riboswitch), a positive selectionmarker is used, or if it represses gene expression (e.g., trpR or B12riboswitch), a negative selection marker is used. To account for bothmodes of gene regulation, tolC gene, a dual selectable marker, is usedas the main selector. Because negative selection of tolC is not nearlyas titratable as positive selection, an inverter circuit as shown inFIG. 2 is provided such that the sensor indirectly activates a positiveselection marker through an intermediate allosteric regulator. In FIG. 2, tetR, which is regulated by B12 riboswitch, is constitutivelyexpressed to shut off the positive selector chloramphenicolacetyltransferase (CAT). When B12 metabolite is present, tetR is nottranslated, thus activating CAT expression.

TtgR sensor-selector construct: A linear DNA fragment (Sequence 4appended at end) comprising a zeocin resistance gene cassette (thePseudomonas putida strain KT2440 TtgR transcriptional regulator gene(Genbank Accession NP_743546.1) codon-optimized for expression in E.coli (Genscript, Piscataway, N.J.), a constitutive promoter apFAB101(5′-AAAAAATTTATTTGCTTTTTATCCCTTGCGGCGATATAATAGATTCATCTTAG), a RBS BB0034(5′-AAAGAGGAGAAATTA) and the 257 basepairs of the Pseudomonas putidastrain KT2440 genome 5′ to the ttgA start codon was constructed byoverlap PCR. This fragment was amplified by PCR with primers eachappending 50 bp of homology to the MG1655 genome at the tolC gene locus(FWD: 5′-AATTTTACAGTTTGATCGCGCTAAATACTGCTTCACCACAAGGAATGCAATCGAACCCCAGAGTCCCGC, REV:5′-CTGAACCCAGAAAGGCTCAGGCCGATAAGAATGGGGAGCAATTTCTTCATGAGGATCCTCGGGTCGCTGGCTGAACCCAGAAAGGCTCAGGCCGATAAGAATGGGGAGCAATTTCTTCATGAGGATCCTCGGGTCGCTGG), and this PCR product was seamlessly integrated intothe genome of ECNR2-T7 5′ to the start codon of the tolC gene usinglambda red recombination. The obtained strain was designated SSECttgR.

CdaR sensor-selector construct: A linear fragment of DNA (Sequence 1Appended to end of this document) containing a beta lactamase expressioncassette, the cdaR gene from E. coli MG1655, a promoter and RBS derivedfrom pZE11 (Lutz and Bujard, Nucleic Acids Res. 1997 25(6):1203-10), andthe 521 basepairs upstream of the E. coli MG1655 gudP start codon wasconstructed by Gibson assembly (Gibson, et al., Nature Meth.6(5):343-345 (2009) and amplified with PCR primers that appendedhomology to the tolC loci of E. coli MG1655 (FW:5-GGCTTCTGCTAGAATCCGCAATAATTTTACAGTTTGATCGCGCTAAATACTGCTTCACCACAAGGAATGCAATCGAACCCCAGAGTCCCG-3 RV:SCTGGCTCAACGAACTGAACCCAGAAAGGCTCAGGCCGATAAGAATGGGGAGCAATTTCTTCATTGTTGCACTCCTGAAAATTCGCGTTAG-3). The linear fragment of DNA was thenintroduced 5′ of the tolC start codon of E. coli strain ECNR2-T7 bylambda red recombineering such that the cloned gudP promoter regiondirected transcription of the tolC gene and the native transcriptionalregulation of the tolC gene was abolished. The obtained strain wasdesignated SSECcdaR.

Example II E. coli Senses Desired Metabolites Using Sensor-Selector

The genetically modified E. coli with the sensor-selector modificationis evaluated and quantified in terms microbe (microorganism)survivability. A dual gradient time course methodology is provided inwhich cells are subjected to increasing selection pressure andincreasing metabolite concentration (exogenously provided). The dualgradient experiment is performed on a 96-well plate by mixing themetabolite to be sensed and toxin in different concentrations to createa gradient. The metabolite is serially diluted along the row (1-12) andtoxin along the column (A-H). A time course experiment is then run todetermine the cell density over time. The time course growth curves arefit using a four-parameter bacterial growth equation. By plotting timeto half maximal growth, across different toxin and metaboliteconcentrations, a heat plot is generated.

Sensing naringenin: FIG. 3 shows a dual gradient heat map for the TtgRsensor-selector in E. coli. by sensing naringenin (metabolite). TtgRenables E. coli to survive the SDS (toxin) in a dose dependent manner.The x-axis is naringenin concentration from low to high (left to right)and y-axis is toxin concentration from low to high (bottom to top). Asshown in FIG. 3 , increasing toxin concentration requires increasingnaringenin to ensure cell survival. In E. coli not containing TtgRsensor-selector, cells grow in presence of toxin with or withoutnaringenin.

Example III Determining Escape Rates of Sensor-Selectors

Spurious activation of the selector (antidote) results in “escapees”that do not respond to the metabolite in a dose-dependent manner. Theseescapees are false positives that do not produce the metabolite at highlevels but eventually take over the population by outcompeting the rest.Before deploying a sensor-selector to identify high producers, the falsepositive rate is determined. In order to determine the false positiverate, serial dilutions of SSECTtgR or SSECCdaR (or any other desiredsensor) are plated on LB-Agar in the absence of the metabolite. Thenumber of colony forming units is a measure of the escape rate. FIG. 4shows the escape rates for the following sensor-selector strains:TtgR-tolC tetR-tolC, btuB riboswitch-CAT, mphR-tolC, cdaR-tolC,xylR-tolC, lacI-tolC, alkS-CAT, benM-tolC and theophylline riboswitch.

Example IV Methods of Reducing Escape Rates

FIG. 5 depicts in schematic methods to reduce failure rates (i.e. escaperates) in sensor-selectors. There are two major sensor-selector failuremodes that causes escape—transcriptional leakage and spontaneousbackground mutants. Since transcription factors and riboswitches are notfully leak proof, basal expression of the selector causes selectionbleed through. Transcriptional leakage can be addressed by changing thesensor or selector modules in the following ways. Sensors derived fromtranscriptional repressors need to be highly expressed to ensure thatthe equilibrium is strongly shifted towards a bound operator site. Thisis accomplished by introducing an additional copy of the sensor or bytandem operator site (demonstrated by Lutz and Bujard, Nucleic AcidsRes. 1997 25(6):1203-10). With an additional copy of TtgR, the falsepositive rate drops by nearly four orders of magnitude.

However, in the case of transcriptional activators, overexpression islikely to lead to spurious selector activation. According to one aspect,feed-forward autoregulation of alkS, where sensor is expressed only inthe presence of the metabolite, leads to reduction in false positives.Selector modifications center around two themes; one, reducing basalfree selector proteins inside the cell, and two, having dual dissimilarselectors under the same sensor. Stochastic variation in freeintracellular selector levels is likely to be sufficient to escapeselection. Therefore, the selector levels are reduced by eitherappending a protein degradation tag to the selector or attenuatingtranslation for changing spacing between Shine-Dalgarno site andtranslation start site. A key tradeoff is balancing reduction in falsepositive rate with a large operational range of the sensor. Forinstance, when the degradation tag is too strong, the sensor isinsensitive to low metabolite concentration, even though false positivesdrop. FIG. 7 shows the inverse relationship between false positive rateand operational range by titration of three ssrA degradation tags ofvarying strengths.

Spontaneous background mutation, another cause for selection escape,occurs at higher rates in the mutator strain used for MAGE (multiplexautomated genome engineering). Further, repeated cycles of MAGE causesdramatic increase in escape rate. However, several iterations of MAGE isrequired for targeting multiple alleles at reasonable efficiencies.Colicin pretreatment is performed that eliminates spontaneous backgroundmutants after MAGE in a dosage dependent manner to restore escape ratesat par with the starting strain.

Dual sensor module: Example: TtgR: An additional copy of TtgR isinserted on the genome, creating a strain in which both ttgR genes mustbe mutated to prevent TtgR from repressing the selector in the absenceof inducer. A linear DNA fragment comprising the promoter apFAB101, RBSBB0034 and codon-optimized ttgR gene from strain SSECttgR and a TetAtetracycline resistance gene cassette was constructed by overlap PCR.This fragment was PCR amplified with primers appending homology to theE. coli MG1655 genome locus 1529620 (FWD:5′-AGCCGGATAAGAAGAGGAAACGCAGCCTAAATAATATCTGGAATAAAGAAAAAAAATTTATTTGCTTTTTATCCCTTGCGGCGA, REV:5′-CCTCTTCACCCTTAATGTCTTTGCAATCTCTTAATAAATTCAGTGCCATCCGCGCCCGGGGAGCCCAAGGGCACGCCCTGGCACCCTGTT) and inserted into the genome of SSECttgRusing lambda red recombination. This obtained strain was designatedSSECttgR2. As shown in FIG. 6 , additional copy of TtgR reduces failurerate by nearly four orders of magnitude.

Selector degradation tag: Example: TtgR sensor-selector with ssrA tags:By adding degradation tag, any free-floating antidote inside the cell isdestabilized. The degradation tag is a short peptide that is appended tothe end of the antidote protein that results in breakdown of anyfree-floating antidote. The strength of the degradation tag determineshow quickly and efficiently the antidote protein can be degraded. A weakdegradation tag may not completely destroy all free-floating antidoteproteins, while a strong degradation tag may destroy the antidoteprotein expressed in response to the target metabolite causing even thehigh producers to perish. Therefore, it is important to tune thedegradation tag strength. FIG. 7 shows the reduction in the failure ratewith degradation tag titration.

The four degradation tags were added to the end of tolC using lambda-redrecombineering using zeocin as the selection marker. The fourdegradation tag sequences are appended to tolC using following primercombinations:

tolC_deg_tag_4_zeo_F TCCGCACGCACTACCACCAGTAACGGTCATAACCCTTTCCGTAACAGGCCTGCAGCAAACGACGAAAACTACGCTTTAGCAGCTTAATGTGTAGGCTGGA GCTGCTTCGtolC_deg_tag_3_zeo_F TCCGCACGCACTACCACCAGTAACGGTCATAACCCTTTCCGTAACAGGCCTGCAGCAAACGACGAAAACTACGCTGCAGCAGTTTAATGTGTAGGCTGGA GCTGCTTCGtolC_deg_tag_2_zeo_F TCCGCACGCACTACCACCAGTAACGGTCATAACCCTTTCCGTAACAGGCCTGCAGCAAACGACGAAAACTACGCTTTAGTAGCTTAATGTGTAGGCTGGA GCTGCTTCGtolC_deg_tag_1_zeo_F TCCGCACGCACTACCACCAGTAACGGTCATAACCCTTTCCGTAACAGGCCTGCAGCAAACGACGAAAACTACGCTGCATCAGTTTAATGTGTAGGCTGGA GCTGCTTCGZeo_tolC_downstream_locus_RTACGTTGCCTTACGTTCAGACGGGGCCGAAGCCCCGTCGTCGTCAAGTTCCTATTCCGAAGTTCCTATTCTCTAGAAAGTAT

Selector RBS attenuation: Example: TtgR sensor-selector with modifiedShine Dalgarno sequences: Titration of degradation tag strength can bedifficult for some sensor-selectors because of large step changes infailure rates with different tags. For finer control, a method isprovided to control antidote protein levels by tuning the ribosomebinding site. The ribosome recognizes a key motif called Shine-Dalgarnosequence that is exactly 7-8 base pairs away from the translation startsite. By varying the spacing and composition between the Shine Dalgarnosequence and translation start site, the ribosome binding affinity istuned to the motif and hence antidote protein translation. By increasingor decreasing the spacing, the amount of spuriously translated proteincan be tightly regulated to reduce false positives.

Dual selectors: Example: TtgR with tolC and TtgR with CAT: By placingthe tolC and CAT genes under independent promoters controlled by theTtgR regulator, two distinct selection mechanisms are used and eitherone if active may kill the cell. A linear DNA fragment comprising atetracycline resistance gene cassette and and the 257 basepairs of thePseudomonas putida strain KT2440 genome 5′ to the ttgA start codon wasconstructed by overlap PCR. This fragment was amplified using PCRprimers (FWD:5′-CGGGCGTATTTTTTGAGTTATCGAGATTTTCAGGAGCTAAGGAAGCTAAACTGTTATAAAAAAAGGATCAATTTTGAACTCTCTCCC, REV:5′-TGCCATTGGGATATATCAACGGTGGTATATCCAGTGATTTTTTTCTCCATGAGGATCCTCGGGTCGCTGGA) appending homology to the E. coli strain EcNR2 genomedirectly 5′ to the chloramphenicol acyltransferase (CAT) locus. Thisconstruct was integrated by lambda red recombination directly 5′ to theCAT gene locus of strain SSECttgR. This dual selector strategy can beused in conjunction with two copies of the ttgR regulator gene.

Autoregulation with feed forward loop: Example: AlkS: To create analkane-sensing strain, AlkS activator from Pseudomonas oleovorans andthe promoter pAlkB whose transcription it controls, have been inserted5′ to tolC gene of EcNR2 (Wang et al. Nature 460, 894-898 2009). To useautoregulation in a feed-forward manner, pAlkB promoter is also used tocontrol the transcription of the alkS gene. This keeps the expression ofAlkS low until alkanes are sensed, increasing the expression of AlkS aswell as its target selector, TolC, in a feed-forward manner whichamplifies the signal and improves sensor-selector robustness.

Pre-screening with negative selection: Example: glucaric acid pathwaystrain: Probability of failure due to occurrence of spontaneous mutantsincreases with the number of MAGE cycles. In FIG. 8 , after 5 cycles ofMAGE in the glucaric acid pathway strain the failure rate increases byseveral orders of magnitude compared to the starting strain. Bypretreatment with negative selection (protein colicin E1), falsepositives can be eliminated in a dose-dependent manner.

Example V Determining Dynamic Range of Sensor by Dual Gradient Heat Plot

The dynamic range is the metabolite concentration range over which thesensor is operational. The sensor cannot detect concentrations below thelower threshold. Above the higher threshold, the sensor is saturated.The dynamic range can be evaluated with a dual gradient heat map. Theupper threshold of the dynamic range denotes the maximum metaboliteconcentration that can be selected for from a population of diversifiedmicrobes.

Example heat plot: TtgR: A dual gradient heat map is generated using themethod of Example 2. In FIG. 9 , the arrow denotes the dynamic range ofTtgR for the metabolite genestein.

Example data for other sensors: TtgR, tetR, btuB riboswitch, mphR, cdaR,xylR, lacI, alkS. benM and theophylline riboswitch [Is there otherdata/heat maps to present for the above?]

Example VI Methods for Modifying Sensor Dynamic Range

According to certain aspects, methods are provided to decrease theeffective intracellular concentration of the sensed molecule byexporting, enzymatically degrading, or sequestering the ligand.

Example using an exporter: tetracycline and TetA membrane pump: The TetRsensor-selector strain responds to the presence of sublethalconcentrations of tetracycline (See FIG. 10 ). By including a copy ofthe tetA gene encoding the TetA tetracycline efflux pump protein, theintracellular concentration of tetracycline is reduced, alleviatingtoxicity and allowing the sensor-selector to confer a growth advantageon the strain to a higher concentration of tetracycline, expanding thedynamic range of the sensor-selector 4-fold by increasing the upperbound concentration (See FIG. 10 ).

Example using degradation enzyme: glucaric acid and gudD glucaratedehydratase: A catabolic enzyme is used to convert the sensed moleculeinto a form that does not activate the sensor response. E. coli enzymeGudD catalyzes the dehydration of D-glucaric acid to5-keto-4-deoxy-D-glucarate (KDG; Gulick et al., Biochemistry39(16):4590-4602 (2000)). By expressing a high level of this enzyme inthe CdaR sensor-selector strain, the response to glucaric acid can bereduced by converting some of the glucaric acid to KDG, which will notbe sensed by CdaR. This shifts the dynamic range to higherconcentrations.

Example using ligand sequestration: B12 and btuB aptamer domain: Anaptamer is expressed within the cell to bind the sensed molecule andreduce its interaction with the sensor. The 5′-untranslated region(5′-UTR) of the E. coli btuB gene contains an aptamer that binds tovitamin B12 and its derivatives (Nahvi et al., Nucleic Acids Res.32:143-150 (2004)). In the btuB riboswitch sensor selector strain, E.coli btuB 5′-UTR is placed 5′ to the tetR regulator gene, controllingits translation; tetR in turn controls the selection gene (See FIG. 2 ).When a high level of the btuB vitamin B12 aptamer domain is transcribedin this strain, it binds to B12 within the cell, sequestering it so thatthere is a lower effective concentration to activate the btuB-tetRsensor. This shifts the dynamic range by increasing the concentrationrequired by the sensor to achieve the same response.

Example VII Toggled Selection for Library of MAGE Mutants (ToSLIMM)

MAGE (multiplex automated genome engineering) is a powerful tool formassively multiplexed engineering of pathway genes (Wang et al. Nature460, 894-898 2009; Wang et al 2012 Nat Methods 9(6):591-3 (2012)). Themethod can generate a genomic library of over a billion variants in aday. The incidence of false positives may increase progressively withMAGE cycles. While a highly diversified population is important forfinding the best producing strain, it also increases the likelihood offinding false positives. Accordingly, Toggled Selection Scheme forLibrary of MAGE Mutants (ToSLIMM) is provided as a method to reducefalse positives resulting from MAGE.

As shown in the schematic of FIG. 11 , the method includes the steps of

-   -   1. Choose a positive-and-negative selectable marker (such as        tolC) as the selector.    -   2. Put the last or last few genes of a pathway on a plasmid.        This ensures that the pathway is incomplete and cannot produce        the final product without these genes. Hence, the        sensor-selector cannot be activated.    -   3. Perform multiple MAGE cycles on all remaining endogenous        targets    -   4. Subject this diversified population to negative selection        (colicin in the case of tolC). This step eliminates all the        spontaneous mutants that are false positives. However, the non        false positives are not killed because the plasmid containing        pathway gene(s) required to complete the pathway is not present        inside the cell. This step ensures that only the “true” false        positives are eliminated.    -   5. Transform the plasmid into the surviving cells and select for        cells containing the plasmid. Now the pathway is complete and        all the cells in the diversified library can be interrogated for        metabolite production.    -   6. Apply positive selection gradient on the transformed cells.        Isolate cells that survive strong selection pressure

Example VIII Naringen Production Pathway

Plasmid construction for heterologous genes: Two plasmids wereconstructed to express 4 heterologous genes for naringenin production.Plasmid 1 contains the p15A origin of replication, a carbenicillinresistance gene cassette, gene RgTALsyn controlled by a pTrc promoterand gene Pc4CLsyn controlled by a second pTrc promoter (Santos et al.,Metabolic Engineering, 13:392-400 (2011)). Plasmid 2 contains a ColEIorigin of replication, a kanamycin resistance gene cassette, genePhCHS-A under control of a pLtetO promoter and gene MsCHI-1 undercontrol of a second pLtetO promoter (Santos, et al., MetabolicEngineering, 13:392-400 (2011); Lutz and Bujard, Nucleic Acids Res. 199725(6):1203-10)). These plasmids were sequenced and naringenin andcoumaric acid production were verified by liquid chromatography-massspectrometry.

Genomic diversification by MAGE targeting naringenin production genes:Naringenin biosynthesis requires tyrosine and malonyl-CoA as inputs fromcellular metabolism (Santos, et al., Metabolic Engineering, 13:392-400(2011)). MAGE mutagenesis (Wang et al., Nature 460, 894-898 (2009)) ofstrain SSECttgR2 was used to create diversity in three pools, targetinggenes shown to be involved in malonyl-CoA overproduction (Xu et al.,Metabolic Engineering, 13:578-587 (2011)), genes shown to be involved intyrosine overproduction (Eversloh et al., Appl. Genetics Mol.Biotechnol. 75:103-110 (2007)), or the conjunction. Genomic diversitytargets for malonyl-CoA overproduction include the following: degeneratestart codons 5′-BTG (fumB, fumC, mdh, acnA); premature stop codons(scpC, sucD); and degenerate ribosome binding sites 5′-DDRRRRRDDDDending −3 bp relative to the start codon (accA, accB, accD, accD, aceE,aceF, lpd, gapA, pgk). Genomic diversity targets for tyrosineoverproduction include the following: premature stop codons (tyrR,trpR); coding mutations shown to alleviate product inhibition (tyrA:M53I, A354V; aroG: D146N); and degenerate ribosome binding sites5′-DDRRRRRDDDD ending −3 bp relative to the start codon (aroG, tyrA,pheA, aspC, tyrB, aroF, aroH, aroK, aroB, ydiB, aroD, aroE, aroL, aroC,aroA). The conjuction genomic target diversity includes all targets fromboth sets.

Selection method for high producer of naringenin: The ToSLIMM protocolwas used to eliminate false positives and identify the best naringeninproducer. The starting strain used for MAGE contains plasmid 1 (genesRgTALsyn and Pc4CLsyn), but no plasmid 2 (genes PhCHS-A and MsCHI-1).Therefore the final two steps of naringenin production remainincomplete. The starting strain is grown LB with carbenicillinresistance of plasmid 1 and diversified through six cycles of MAGE.After the sixth cycle, cells are grown in media containing LB with Carband a gradient of colicin concentrations across a 96-well. Since,negative selection (with colicin) shouldn't affect the regular cells,the gradient is helpful in determining the maximum negative selectionpressure that does not place a growth burden on the population. Thepopulation is then chosen from the highest colicin concentration withleast burden and grown out in LB-carb medium. At mid-log, the cells areharvested, washed to make them electrocompetent and transformed withplasmid 2. After recovery, the transformed cells are grown inLB-Carb-Kan and IPTG overnight to enable production of naringenin.

Approximately 10{circumflex over ( )}7 cells of overnight culture areadded to each well of a 96-well plate where a gradient of positiveselection pressure (with SDS) is applied. The cell density is monitoredin a time course experiment over 24 hours. The cells that grow understrong selection pressure are isolated, regrown and assayed fornaringenin production of LC-MS. FIG. 12 shows the result of the ToSLIMMprotocol applied to naringenin pathway. The best producing strain hasnaringenin levels nearly 15 fold above the starting strain.

Example IX Glucaric Acid Production Pathway

Plasmid construction of heterologous genes: A plasmid for glucaric acidbiosynthesis was constructed from four PCR fragments by Gibson Assembly(Gibson et al., Nature Meth. 6(5):343-345 (2009)). The first fragmentwas amplified by PCR from pZE22 (Lutz and Bujard, Nucleic Acids Res.1997 25(6):1203-10) and contained the ColE1 origin of replication andKanamycin resistance marker. The second fragment was amplified by PCRfrom a Myo-inositol-oxygenase (MIOX) gene derived from Mus musculus andsynthesized in an E. coli codon optimized form by Genscript. The forwardprimer was used to introduce a T7 promoter and RBS (FW primer:5-TGCTAGCAAGTAAGGCCGACTAATACGACTCACTATAGGGAGAAAGAAGGAGGTAACTCATAGTGAAAGTGGATGTTGGCCCGGA-3). The third fragment was amplified by PCRfrom the Saccharomyces cerevisiae genome and contained the geneinositol-1-phosphate synthase (IN01) (FW primer:5-TAAGAATTCATTAAAGAGGAGAAAGAATTCATGACAGAAGATAATATTGCTCCAATCACC-3 RVprimer:5-ATGGTACCTTTCTCCTCTTTAATGGTACCTTACAACAATCTCTCTTCGAATCTTAGTTCG-3). Thefourth fragment was amplified by PCR from the genome of Agrobacteriumtumefaciens and contained the gene uronate dehydrogenase (UDH). Theplasmid was designated pT7GAEXP (Sequence 2).

Genomic diversification by MAGE targeting glucaric acid productiongenes: MAGE (Wang et al. Nature 460, 894-898 (2009)) was used to changethe SSECcdaR genome in seven locations. The genes garK and uxaC wereeach targeted for complete knockout by the introduction of two prematurestop codons. The genes suhB, pgi, sthA, zef and mdh were modified attheir ribosomal binding sites (RBS). Degenerate oligomers were used tointroduce semi-random RBS at each gene in order to span a complete rangeof expression levels. Each cell in the diversified population maycontain zero to seven genomic modifications. Two loci have twopossibilities (premature stop codons or unchanged) while five loci have1.8×10⁵ possible ribosomal binding sites. The theoretical library sizeafter MAGE would be 7.0×10²⁶, however practicality limits this toroughly one billion. The oligomers used are listed as Sequence 3[Appended]. Five cycles of MAGE were completed. The resulting collectionof strains was designated SSECcdaR-D.

Diversification of glucaric acid production plasmid: MIOX was amplifiedby PCR from the pT7GAEXP plasmid using a single forward primer(5-ATGAAAGTGGATGTTGGCCCGGAC-3) and a mixture of degenerate reverseprimers(5-CTTTAACGGAGGTGATTGGAGCAATATTATCTTCTGTCATGAATTCTTYYBYYYYTTTAATGAATTCTTACCACGACAGGGTGCCCGGAC-3). INO1 was amplified by PCR from thepT7GAEXP plasmid (Forward primer:5-AAGAATTCATGACAGAAGATAATATTGCTCCAATC-3 Reverse primer:5-TTTAATGGTACCTTACAACAATCTCTCTTCGAATC-3). The PCR products of MIOX andINO1 were assembled by overlap extension PCR (citation) to create asingle construct with a single degenerate RBS preceding the INO1 gene.The MIOX-(semi-random-RBS)-INO1 construct was amplified by PCR with twodegenerate primers that also contained Bsa1 restriction sites (Forwardprimer: 5-AACGAACCAGAACCTGCAGGAATTCCACACCAGGTCTCAAGAATTCATTAAARRRRVRRAAGGTACCATGAAAGTGGATGTTGG-3 Reverse primer:5-GCGGTTGTTGAAGGTATCCGTAAACCACACCAGGTCTCAGGTACTTTYYBYYYYTTTAATGGTACCTTACAACAATCTCTCTTCG-3) to create a PCR product that contained threelocations with degenerate bases. This PCR product was again amplified byPCR with primers annealing at the extreme ends of the template (Forwardprimer: 5-AACGAACCAGAACCTGCAGGAATTC-3 Reverse primer:5-GCGGTTGTTGAAGGTATCCGTAAAC-3). The backbone and UDH gene were amplifiedby PCR from pT7GAEXP with primers that contained Bsa1 restriction sites(Forward primer: CACACCAGGTCTCATACCATGAAACGGCTTCTTGTTAC Reverse primer:CACACCAGGTCTCATTCTCTCCCTATAGTGAGTCGTATTAGTCG). Both the degenerateinsert and the vector were digested with Bsa1 restriction enzyme (NewEngland Biolabs) and ligated with T4 DNA ligase (New England Biolabs).The resulting plasmid had a theoretical library size of 7 million andwas designated pT7GAEXP-degen.

Selection of high producer of glucaric acid from genomic library: TheToSLIMM protocol was used to eliminate false positives and identify thebest glucaric acid producer. The collection of strains SSECcdaR-D wasgrown overnight to saturation. The saturated culture was inoculated to 3ml fresh LB at a dilution of 1:100. Colicin was added at a ratio of1:10. The culture was grown at 30° C. for 48 hours. This culture wasthen diluted 1:100 into 3 ml fresh LB and grown to an OD 0.5. The cellswere harvested by centrifugation and washed twice with deionized waterat 4° C. 100 ng of plasmid pT7GAEXP was electroporated into SSECcdaR-D.The cells were then grown for 1 hour in outgrowth media before beingdiluted 20 fold into LB supplemented with 50 ug/ml kanamycin. The cellswere grown to saturation overnight. The cells were back-diluted 1:100 inLB supplemented with 50 ug/ml kanamycin, 10 mM glucose and 1 mM IPTG.After 24 hours this culture was used to inoculate 48 micro-titer wellsat a 1:100 dilution. Each well contained LB supplemented with 10 mMglucose, 1 mM IPTG, 50 ug/ml kanamycin and 0.005% SDS. The correct SDSconcentration was determined from a previous experiment characterizingthe sensor response to glucaric acid. The selection plate was thenmonitored for absorbance at 600 nm while incubating with shaking at 30°C. Wells that showed growth were used to inoculate non-selectivecultures for further analysis. FIG. 13 shows the results of ToSLIMMprotocol applied to the glucaric acid pathway. The best producingstrains produced glucaric acid on average 415 fold above the parentstrain.

Selection of high producer from plasmid library: SSECcdaR was grown tosaturation and harvested by centrifugation. The cell pellet was washedtwice with deionized water and electroporated with 100 ng of the libraryof diversified plasmids pT7GAEXP-degen. The cells were then grown for 1hour in outgrowth media before being diluted 20 fold into LBsupplemented with 50 ug/ml kanamycin. The cells were grown to saturationovernight. The cells were back-diluted 1:100 in LB supplemented with 50ug/ml kanamycin, 10 mM glucose and 1 mM IPTG. After 24 hours the culturewas used to inoculate 96 micro-titer wells at a 1:100 dilution. Eachwell contained LB supplemented with 1 mM IPTG, 50 ug/ml kanamycin and0.005% SDS. The amount of glucose was varied between 50 mM and 3 mM inorder to challenge the cells with differing selective pressures. Theselection plate was then monitored for absorbance at 600 nm whileincubating with shaking at 30° C. Wells that showed growth were used toinoculate non-selective cultures for further analysis.

The contents of all references, patents and published patentapplications cited throughout this application are hereby incorporatedby reference in their entirety for all purposes.

EQUIVALENTS

Other embodiments will be evident to those of skill in the art. Itshould be understood that the foregoing description is provided forclarity only and is merely exemplary. The spirit and scope of thepresent invention are not limited to the above example, but areencompassed by the claims. All publications, patents and patentapplications cited above are incorporated by reference herein in theirentirety for all purposes to the same extent as if each individualpublication or patent application were specifically indicated to be soincorporated by reference.

APPENDIX Sequence 1: Glucaric Acid Sensor ModuleGGCTTCTGCTAGAATCCGCAATAATTTTACAGTTTGATCGCGCTAAATACTGCTTCACCACAAGGAATGCAATCGAACCCCAGAGTCCCGCTCAGAAGAACTCGTCAAGAAGGCGATAGAAGGCGATGCGCTGCGAATCGGGAGCGGCGATACCGTAAAGCACGAGGAAGCGGTCAGCCCATTCGCCGCCAAGCTCTTCAGCAATATCACGGGTAGCCAACGCTATGTCCTGATAGCGGTCCGCCACACCCAGCCGGCCACAGTCGATGAATCCAGAAAAGCGGCCATTTTCCACCATGATATTCGGCAAGCAGGCATCGCCATGGGTCACGACGAGATCCTCGCCGTCGGGCATGCGCGCCTTGAGCCTGGCGAACAGTTCGGCTGGCGCGAGCCCCTGATGCTCTTCGTCCAGATCATCCTGATCGACAAGACCGGCTTCCATCCGAGTACGTGCTCGCTCGATGCGATGTTTCGCTTGGTGGTCGAATGGGCAGGTAGCCGGATCAAGCGTATGCAGCCGCCGCATTGCATCAGCCATGATGGATACTTTCTCGGCAGGAGCAAGGTGAGATGACAGGAGATCCTGCCCCGGCACTTCGCCCAATAGCAGCCAGTCCCTTCCCGCTTCAGTGACAACGTCGAGCACAGCTGCGCAAGGAACGCCCGTCGTGGCCAGCCACGATAGCCGCGCTGCCTCGTCCTGCAGTTCATTCAGGGCACCGGACAGGTCGGTCTTGACAAAAAGAACCGGGCGCCCCTGCGCTGACAGCCGGAACACGGCGGCATCAGAGCAGCCGATTGTCTGTTGTGCCCAGTCATAGCCGAATAGCCTCTCCACCCAAGCGGCCGGAGAACCTGCGTGCAATCCATCTTGTTCAATCATGCGAAACGATCCTCATCCTGTCTCTTGATCAGATCTTGATCCCCTGCGCCATCAGATCCTTGGCGGCAAGAAAGCCATCCAGTTTACTTTGCAGGGCTTCCCAACCTTACCAGAGGGCGCCCCAGCTGGCAATTCCctaCCGCTCTTCATCCAGTTGTAACGCCACATACAGCAGCAACCTGTCATCAAAATTGCCCAAATCAAGCCCGGTCAGTTCCGATATACGATTAAGCCGATACTCCAGGGTATTACGATGAATAAACAACGCCTTTGACGTTGCCAGCGGTTGCACATTGTGGCGAAACCACGCCGCCAGCGTTCGTCGCAGCAAGCCGTTATTGTCCATCGTTTTCAGCCGCGCCAGCGGTCGCGCCAGTTCGTTGGCCTGCCAGTCGCCACGCAAACTGTCGAGTAACACAGGTAACATCAGATCCTGATAAAAATAGCAGCGACTTTCTGGCATCCGCTGTTTACCCACCACCATCGTCGTTTTCGCCGTACGATAGGATCGGGCAATACTGCCAGGACCGGTAAAATAGTTGCCCAGTGAAACGCGAAAACGCAGCTGGCCGTACTCTTTCATGCGGGTAATCAGTTGTTCAACTCGCTTACGATGATCTTCTGCATCCCAGCGCCCAAAAGAGTTCAACGCCGGTTTCAACACCACCATTTCGGTTAGCGAGACAATCGCCACCAGATTATTACGCTCGGGCGTAGTCAGCGCGTTTTGCAGTTGTTGTAACTCCGCCATTGCGCTGTCCACGCCAAGCTGACCGCTGTCGACCTCAACAATAGCCACCACTCGCGGTTGATTGAGATCGATCCCCAGCCGTTGCGCCCATTCAGTAAGTGCGGGAGTATTCTCCTCTGCCTGAATCAGGTTCATCACCAGTTCTTCCCGCAAACGGCTATCCTGCGCCAACAAGTGCATCAACCGCGACTGTTCCAGCATCATTTCAGCCGTCATGCAGACCAGTTCGCCATATTTACGCAGATTCTCTGGTTCACCTGTCAGGCCAATTACGCCGACAATTTCACCTTCCAGCCGTAACGGTAGATTAATCCCCTGCCGCACACCGTGCAGATGACGTGCTACCGCGTCATCGATATCGACGACTCGTCCCTGTGAAAGTACCAGCAATGCACCTTCGTGCAATTCACCAATACGCTCACGATCGCCGCTGCCGATAATTCGCCCACGGGCATCCATTACGTTGATATTGGTATCGATGATGCGCATGGTACGTGCCACGATATCCTGCGCCATTTTGGTATCAAGATGCCAGCCAGCcatGGTACCTTTCTCCTCTTTAATGAATTCGGTCAGTGCGTCCTGCTGATGTGCTCAGTATCTCTATCACTGATAGGGATGTCAATCTCTATCACTGATAGGGACTCGAGGTGAAGACGAAAGGGCCTCGTGATACGCCTATTTTTATAGGTTAATGTCATGATAATAATGGTTTCTTAGACGTCGGAATTGATGCTGTTGATTGACGCCAGTGAGAACCCGGAACCGGAAACGGAATCAAATCCGTGGGTCGAACAGTGGGGCACGCTGTTGTCCTGATATGTTCAGCGAGCGGTAAATGTCGTTTTAGCGGTGCTGAATCGAATCTTTTCAGGCAAATGCCAGTAAAAACTGCTTCATAGCGCGGATTTTTACTGGCGTTTGCCTGGAGTCAAGCGATCCATTTCATACTCTTCTTTATTTCTTCGTTTTAACCCTTCCTTTCTTGTTCTTGTTTTCATTTCCGTGAAGTGGATTCCACCGTCCAGGGCTAATGCCAAAATCGGGCCTCATTGAACGCATTAATGTTGTGTTGTTGCACGGTGAGCCGCTATGGCGCGCTTTTTATACTGCTATTGCCAGATATAAACACGCGCCGTATTCGGCGAACGACCTATAAAAACGGCAAAAAACACCCTACGTCACCTCTGATTTCCTGGCGATGTCGCAGTCCAGAGTGAGCGTGGCTAACGCGAATTTTCAGGAGTGCAACAATGAAGAAATTGCTCCCCATTCTTATCGGCCTGAGCCTTTCTGGGTTCAGTTCGTTGAGC CAGSequence 2: pT7GAEXP ACTAGTGCTTGGATTCTCACCAATAAAAAACGCCCGGCGGCAACCGAGCGTTCTGAACAAATCCAGATGGAGTTCTGAGGTCATTACTGGATCTATCAACAGGAGTCCAAGCGAGCTCTCGAACCCCAGAGTCCCGCTCAGAAGAACTCGTCAAGAAGGCGATAGAAGGCGATGCGCTGCGAATCGGGAGCGGCGATACCGTAAAGCACGAGGAAGCGGTCAGCCCATTCGCCGCCAAGCTCTTCAGCAATATCACGGGTAGCCAACGCTATGTCCTGATAGCGGTCCGCCACACCCAGCCGGCCACAGTCGATGAATCCAGAAAAGCGGCCATTTTCCACCATGATATTCGGCAAGCAGGCATCGCCATGGGTCACGACGAGATCCTCGCCGTCGGGCATGCGCGCCTTGAGCCTGGCGAACAGTTCGGCTGGCGCGAGCCCCTGATGCTCTTCGTCCAGATCATCCTGATCGACAAGACCGGCTTCCATCCGAGTACGTGCTCGCTCGATGCGATGTTTCGCTTGGTGGTCGAATGGGCAGGTAGCCGGATCAAGCGTATGCAGCCGCCGCATTGCATCAGCCATGATGGATACTTTCTCGGCAGGAGCAAGGTGAGATGACAGGAGATCCTGCCCCGGCACTTCGCCCAATAGCAGCCAGTCCCTTCCCGCTTCAGTGACAACGTCGAGCACAGCTGCGCAAGGAACGCCCGTCGTGGCCAGCCACGATAGCCGCGCTGCCTCGTCCTGCAGTTCATTCAGGGCACCGGACAGGTCGGTCTTGACAAAAAGAACCGGGCGCCCCTGCGCTGACAGCCGGAACACGGCGGCATCAGAGCAGCCGATTGTCTGTTGTGCCCAGTCATAGCCGAATAGCCTCTCCACCCAAGCGGCCGGAGAACCTGCGTGCAATCCATCTTGTTCAATCATGCGAAACGATCCTCATCCTGTCTCTTGATCAGATCTTGATCCCCTGCGCCATCAGATCCTTGGCGGCAAGAAAGCCATCCAGTTTACTTTGCAGGGCTTCCCAACCTTACCAGAGGGCGCCCCAGCTGGCAATTCCtgctagcaagtaaggccgactaatacgactcactatagggagaaagaaggaggtaactcataGTGAAAGTGGATGTTGGCCCGGACCCGAGCCTGGTTTACCGCCCGGATGTGGACCCGGAAATGGCAAAAAGCAAAGATTCGTTTCGTAACTACACCAGTGGCCCGCTGCTGGATCGTGTTTTTACCACGTATAAACTGATGCATACCCACCAGACGGTTGACTTTGTCAGCCGTAAACGCATTCAATATGGCGGTTTCTCTTACAAGAAAATGACCATCATGGAAGCGGTGGGCATGCTGGATGACCTGGTTGATGAATCAGATCCGGACGTCGATTTTCCGAATTCGTTTCATGCGTTCCAGACGGCCGAAGGTATTCGCAAAGCCCACCCGGACAAAGATTGGTTCCATCTGGTCGGCCTGCTGCACGATCTGGGTAAAATCATGGCACTGTGGGGTGAACCGCAGTGGGCTGTGGTTGGTGATACCTTTCCGGTGGGTTGCCGTCCGCAAGCAAGTGTCGTGTTTTGTGACTCCACCTTCCAGGACAACCCGGATCTGCAAGACCCGCGCTATTCAACGGAACTGGGCATGTACCAGCCGCATTGCGGTCTGGAAAACGTGCTGATGTCGTGGGGTCACGATGAATACCTGTACCAGATGATGAAATTCAACAAATTCAGCCTGCCGTCTGAAGCCTTCTACATGATCCGTTTCCATAGTTTCTACCCGTGGCACACCGGCGGTGATTATCGCCAGCTGTGCTCCCAGCAAGACCTGGATATGCTGCCGTGGGTGCAAGAATTCAACAAATTCGATCTGTACACGAAATGTCCGGATCTGCCGGACGTTGAATCTCTGCGTCCGTACTACCAAGGTCTGATTGATAAATACTGTCCGGGCACCCTGTCGTGGTAAGAATTCATTAAAGAGGAGAAAGAATTCATGACAGAAGATAATATTGCTCCAATCACCTCCGTTAAAGTAGTTACCGACAAGTGCACGTACAAGGACAACGAGCTGCTCACCAAGTACAGCTACGAAAATGCTGTAGTTACGAAGACAGCTAGTGGCCGCTTCGATGTAACGCCCACTGTTCAAGACTACGTGTTCAAACTTGACTTGAAAAAGCCGGAAAAACTAGGAATTATGCTCATTGGGTTAGGTGGCAACAATGGCTCCACTTTAGTGGCCTCGGTATTGGCGAATAAGCACAATGTGGAGTTTCAAACTAAGGAAGGCGTTAAGCAACCAAACTACTTCGGCTCCATGACTCAATGTTCTACCTTGAAACTGGGTATCGATGCGGAGGGGAATGACGTTTATGCTCCTTTTAACTCTCTGTTGCCCATGGTTAGCCCAAACGACTTTGTCGTCTCTGGTTGGGACATCAATAACGCAGATCTATACGAAGCTATGCAGAGAAGTCAAGTTCTCGAATATGATCTGCAACAACGCTTGAAGGCGAAGATGTCCTTGGTGAAGCCTCTTCCTTCCATTTACTACCCTGATTTCATTGCAGCTAATCAAGATGAGAGAGCCAATAACTGCATCAATTTGGATGAAAAAGGCAACGTAACCACGAGGGGTAAGTGGACCCATCTGCAACGCATCAGACGCGATATCCAGAATTTCAAAGAAGAAAACGCCCTTGATAAAGTAATCGTTCTTTGGACTGCAAATACTGAGAGGTACGTAGAAGTATCTCCTGGTGTTAATGACACCATGGAAAACCTCTTGCAGTCTATTAAGAATGACCATGAAGAGATTGCTCCTTCCACGATCTTTGCAGCAGCATCTATCTTGGAAGGTGTCCCCTATATTAATGGTTCACCGCAGAATACTTTTGTTCCCGGCTTGGTTCAGCTGGCTGAGCATGAGGGTACATTCATTGCGGGAGACGATCTCAAGTCGGGACAAACCAAGTTGAAGTCTGTTCTGGCCCAGTTCTTAGTGGATGCAGGTATTAAACCGGTCTCCATTGCATCCTATAACCATTTAGGCAATAATGACGGTTATAACTTATCTGCTCCAAAACAATTTAGGTCTAAGGAGATTTCCAAAAGTTCTGTCATAGATGACATCATCGCGTCTAATGATATCTTGTACAATGATAAACTGGGTAAAAAAGTTGACCACTGCATTGTCATCAAATATATGAAGCCCGTCGGGGACTCAAAAGTGGCAATGGACGAGTATTACAGTGAGTTGATGTTAGGTGGCCATAACCGGATTTCCATTCACAATGTTTGCGAAGATTCTTTACTGGCTACGCCCTTGATCATCGATCTTTTAGTCATGACTGAGTTTTGTACAAGAGTGTCCTATAAGAAGGTGGACCCAGTTAAAGAAGATGCTGGCAAATTCGAGAACTTTTATCCAGTTTTAACCTTCTTGAGTTACTGGTTAAAAGCTCCATTAACAAGACCAGGATTTCACCCGGTGAATGGCTTAAACAAGCAAAGAACCGCCTTAGAAAATTTTTTAAGATTGTTGATTGGATTGCCTTCTCAAAACGAACTAAGATTCGAAGAGAGATTGTTGTAAGGTACCATTAAAGAGGAGAAAGGTACCATGAAACGGCTTCTTGTTACCGGTGCGGCGGGCCAGCTTGGCCGCGTCATGCGCGAGCGTCTCGCACCGATGGCGGAGATACTGCGCCTTGCCGATCTCTCCCCGCTCGACCCGGCAGGGCCGAACGAAGAATGCGTGCAATGCGACCTTGCCGATGCCAATGCCGTGAATGCCATGGTCGCCGGTTGCGACGGTATTGTTCATCTCGGCGGCATCTCGGTGGAGAAGCCCTTCGAACAAATCCTTCAGGGCAATATCATCGGGCTTTATAATCTCTACGAGGCCGCCCGCGCCCATGGACAGCCACGCATCGTCTTTGCCAGCTCCAACCACACGATCGGCTATTATCCGCAGACCGAACGGCTCGGTCCGGATGTTCCGGCGCGGCCGGACGGTCTTTACGGCGTCTCCAAATGTTTCGGCGAAAACCTCGCCCGCATGTATTTCGATAAATTCGGGCAGGAGACGGCGCTGGTGCGCATCGGCTCCTGTACGCCGGAACCCAACAATTACCGCATGCTGTCCACCTGGTTTTCGCACGATGATTTCGTGTCGCTGATCGAGGCGGTGTTTCGCGCGCCGGTGCTCGGCTGCCCGGTCGTCTGGGGGGCATCGGCCAATGATGCGGGCTGGTGGGACAATTCGCATCTTGGCTTTCTGGGCTGGAAACCGAAGGATAATGCCGAGGCCTTCCGGCGGCATATAACCGAGACGACACCGCCACCGGACCCGAATGACGCGTTGGTGCGGTTCCAGGGCGGTACGTTTGTCGACAACCCGATCTTCAAACAGAGCTGAAAGCTTGATATCGAATTCCTGCAGCCCGGGGGATCCCATGGTACGCGTGCTAGAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCCTGAGTAGGACAAATCCGCCGCCCTAGACCTAGGCGTTCGGCTGCGGCGAGCGGTATCAGCTCACTCAAAGGCGGTAATACGGTTATCCACAGAATCAGGGGATAACGCAGGAAAGAACATGTGAGCAAAAGGCCAGCAAAAGGCCAGGAACCGTAAAAAGGCCGCGTTGCTGGCGTTTTTCCATAGGCTCCGCCCCCCTGACGAGCATCACAAAAATCGACGCTCAAGTCAGAGGTGGCGAAACCCGACAGGACTATAAAGATACCAGGCGTTTCCCCCTGGAAGCTCCCTCGTGCGCTCTCCTGTTCCGACCCTGCCGCTTACCGGATACCTGTCCGCCTTTCTCCCTTCGGGAAGCGTGGCGCTTTCTCAATGCTCACGCTGTAGGTATCTCAGTTCGGTGTAGGTCGTTCGCTCCAAGCTGGGCTGTGTGCACGAACCCCCCGTTCAGCCCGACCGCTGCGCCTTATCCGGTAACTATCGTCTTGAGTCCAACCCGGTAAGACACGACTTATCGCCACTGGCAGCAGCCACTGGTAACAGGATTAGCAGAGCGAGGTATGTAGGCGGTGCTACAGAGTTCTTGAAGTGGTGGCCTAACTACGGCTACACTAGAAGGACAGTATTTGGTATCTGCGCTCTGCTGAAGCCAGTTACCTTCGGAAAAAGAGTTGGTAGCTCTTGATCCGGCAAACAAACCACCGCTGGTAGCGGTGGTTTTTTTGTTTGCAAGCAGCAGATTACGCGCAGAAAAAAAGGATCTCAAGAAGATCCTTTGATCTTTTCTACGGGGTCTGACGCTCAGTGGAACGAAAACTCACGTTAA GGGATTTTGGTCATGSequence 3: MAGE oligomers used to create SSECcdaR-D >garK 2 premature stopT*T*C*C*CGAAATCCTTTTTCTATCGCCTGCGCAACCTCGCTGGCAGATTAACTTTCTTTTTAAGAGTCTGGGGCGATTACGATTTTCATACC >uxaC 2 premature stopC*A*A*A*TGGCAATGGTAATCGAAAATCGGCTGGTCTTTTGCTTAGTCGTGTTACAGACGGCGGGCAAATTCGGTATCTAACAGGAAATCTTC >suhB RBSC*T*C*G*CTGCTATACTCTGCGCCGTTTTCCCGTTCTTTAACATCCDDVVVVVDDDDCCGatgCATCCGATGCTGAACATCGCCGTGCGCGCA >pgi RBSG*G*C*A*GCGGTCTGCGTTGGATTGATGTTTTTCATTAGHHHHBBBBBHHTGATTTTGAGAATTGTGACTTTGGAAGATTGTAGCGCCAGTCA >sthA RBSC*G*C*G*ATAAAATGTTACCATTCTGTTGCTTTTATGTATAAGAACDDVVVVVDDDDACCatgCCACATTCCTACGATTACGATGCCATAGTA >zwf RBSG*T*C*A*CAGGCCTGGGCTGTTTGCGTTACCGCCATGTCHHHHBBBBBHHGTTAACTAACCCGGTACTTAAGCCAGGGTATACTTGTAATTTT >mdh RBSA*C*C*G*CCAGCAGCGCCGAGGACTGCGACTTTCATCCTHHHHBBBBBHHTATATTGATAAACTAAGATATGTTGCTCCGCTGCCGCGACCTTSequence 4: TtgR sensor module with zeocin resistance gene cassette5′-TCGAACCCCAGAGTCCCGCAGTTCCTATTCCTtAGTCCTGCTCCTCGGCCACGAAGTGCACGCAGTTGCCGGCCGGGTCGCGCAGGGCGAACTCCCGCCCCCACGGCTGCTCGCCGATCTCGGTCATGGCCGGCCCGGAGGCGTCCCGGAAGTTCGTGGACACGACCTCCGACCACTCGGCGTACAGCTCGTCCAGGCCGCGCACCCACACCCAGGCCAGGGTGTTGTCCGGCACCACCTGGTCCTGGACCGCGCTGATGAACAGGGTCACGTCGTCCCGGACCACACCGGCGAAGTCGTCCTCCACGAAGTCCCGGGAGAACCCGAGCCGGTCGGTCCAGAACTCGACCGCTCCGGCGACGTCGCGCGCGGTGAGCACCGGAACGGCACTGGTCAACTTGGCCATGGTTTAGTTCCTCACCTTGTCGTATTATACTATGCCGATATACTATGCCGATGATTAATTGTCAACACGTTTATTTGCGCAGCGCCGGGCTCAGACGCAGCATATCCAGACCGGTATCAACCCATTTTTCCACATCGCCCAGCAGATCAACACTATCCGGCAGCAGCAGCCAACGACCAATCAGGCCATCCACATAGGCAAACATCGCAACCGCTGCGCGTTCCACATCCAGTTCACCCGGCAGCTGACCGCGACGAACTGCGTTTGCCAGTGCCAGGGTGATACCTTTATGACAATCCAGCACGGCGCTCTGGCGCTGCTGACGAATTTCACACATATCATCCGTAAATTCGCATTTGTGATGCAGGATTTCATTAATGCGACGGGTACGTGCATCCAGAACCAGTTCGTTAAACACCTGCAGCAGCAGTTTGCGCATGCAGCCCAGCGGGTCCAGTTCATCTTCAGATTCGCTTGCACGGGCCAGGTGATCATGCGTTTCGTGCAGAGAATCCAGCAGTGCCTGAACCAGTTCGGCTTTATTGTTGAAATGCCAGTAGATTGCACCGCGGGTAACACCTGCCAGTTCTGCAATATCTGCCAGCGTGGTACGTGCCACACCACGTTTATAAAACGCGCGTTCGGCCGCTTCGATAATCTGCGCACGCGTTTCCTGTGCTTCTTCTTTGGTGCGACGCACCATTAATTTCTCCTCTTTCTAAGATGAATCTATTATATCGCCGCAAGGGATAAAAAGCAAATAAATTTTTTGGCAGTACAACCTCATCTGGCCCGAACGCGCAGCGCGTGCGGGGAACGCTCCGGGGGCACCTCTGCCGGGGCCTCTCGGATAAGCTAATCAAGCGCTGAGGCTGTACCTGAGTACCACCCAGCAGTATTTACAAACAACCATGAATGTAAGTATATTCCTTAGCAAGCTATTTATCCACCGGATAGCATTTTTTCCAGATCAAGATCTCTTCCATTACTCTTGAGCGTCTCCCTGCTCCAGCGACCCGAGGATCCTC

What is claimed is:
 1. A method of selecting a subset of microbes forthe production of a metabolite comprising placing a population ofmicrobes in an environment of a toxin, wherein the toxin is external tothe population of microbes, wherein the population of microbes producesa sensor biomolecule that regulates production of an antidote to atoxin, wherein the population of microbes produces a metabolite bindingpartner of the sensor biomolecule, which binds to the sensor to induceproduction of the antidote in a manner dependent on the concentration ofthe produced metabolite, wherein the toxin and antidote pair is selectedfrom the group consisting of SDS:tolC, colicin:tolC (negativeselection), kanamycin:kanamycin nucleotidyltransferase, chloramphenicol:chloramphenicol acyl transferase, ampicillin:beta lactamase,tetracycline:tetracycline efflux pump tetA, nickel chloride:tetracyclineefflux pump tetA (negative selection), and 5-fluoroorotic acid:URA3(negative selection), and selecting a subset of microbes that producesufficient metabolite to prevent microbe death.
 2. The method of claim 1further comprising genetically modifying the subset of microbes to altergenes that affect production of the metabolite directly or indirectly,subjecting the subset of microbes to a subsequent environment of thetoxin having a concentration greater than the previous environment, andselecting a subsequent subset of microbes the produce sufficientmetabolite to prevent microbe death.
 3. The method of claim 2 furthercomprising repeating in sequence: (1) genetically modifying thesubsequent subset of microbes by altering genes that affect theproduction of the metabolite, (2) subjecting the genetically alteredmicrobes to a subsequent environment of a toxin having a concentrationgreater than a previous environment, and (3) selecting a furthersubsequent subset of microbes that produce sufficient metabolite toprevent microbe death, said repeating step resulting in optimizedmetabolite producing microbes.
 4. The method of claim 1 wherein bindingof the metabolite to the sensor regulates production of the antidote ina manner dependent on the concentration of the produced metabolite. 5.The method of claim 4 wherein a positive selection marker is used toselect the subset of microbes that produce sufficient metabolite toprevent microbe death.
 6. The method of claim 4 wherein the sensorregulates production of two or more antidotes independently and two ormore toxins are used to select the subset of microbes that producesufficient metabolite to prevent microbe death.
 7. The method of claim 4wherein a negative selection marker is used to eliminate false positivesthat detoxify the microbe despite not producing sufficient metabolite.8. The method of claim 1 wherein the population of microbes has beengenetically modified to encode two or more redundant copies of thesensor in order to reduce false positives.
 9. The method of claim 1wherein the sensor also regulates its own expression through a cognatenucleic acid sequence located 5′ to the DNA sequence encoding the sensorin order to reduce false positives.
 10. The method of claim 1 whereinthe degradation rate of the antidote is increased by a degradationsignal attached to the antidote in order to reduce false positives. 11.The method of claim 2 wherein the step of genetically modifying thesubset of microbes to alter genes that produce the metabolite includesmultiplexed automated genome engineering.
 12. The method of claim 2wherein the step of genetically modifying the subset of microbesincludes making a plasmid library of pathway genes.
 13. The method ofclaim 2 wherein the step of genetically modifying the subset of microbesincludes making a plasmid library of genomic fragments of any organism.14. The method of claim 2 wherein the step of genetically modifying thesubset of microbes includes making a plasmid library of metagenomicsequences.
 15. The method of claim 11 wherein the multiplexed automatedgenome engineering includes reducing spontaneous background mutants. 16.The method of claim 11 wherein the multiplexed automated genomeengineering includes reducing spontaneous background mutants bypretreatment with a negative selector.
 17. The method of claim 1 whereinconcentration of the metabolite exposed to the sensor is attenuated. 18.The method of claim 17 wherein the concentration of the metaboliteexposed to the sensor is attenuated by expressing one or more proteinsto export the metabolite outside of the cell.
 19. The method of claim 17wherein the concentration of the metabolite exposed to the sensor isattenuated by expressing one or more enzymes that convert the metaboliteto another metabolite having less interaction with the sensor.
 20. Themethod of claim 17 wherein the concentration of the metabolite exposedto the sensor is attenuated by expressing a biomolecule that binds tothe metabolite and reduces its interaction with the sensor.